import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_big_bird import BigBirdTokenizer
else:
		__a    :	str         =    None

__a    :	Optional[int]         =    logging.get_logger(__name__)
__a    :	Any         =    {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__a    :	List[str]         =    {
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    },
    """tokenizer_file""": {
        """google/bigbird-roberta-base""": (
            """https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"""
        ),
    },
}

__a    :	Any         =    {
    """google/bigbird-roberta-base""": 4_0_9_6,
    """google/bigbird-roberta-large""": 4_0_9_6,
    """google/bigbird-base-trivia-itc""": 4_0_9_6,
}


__a    :	List[str]         =    """▁"""




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					Dict     					=		VOCAB_FILES_NAMES
			__a    :					Union[str, Any]     					=		PRETRAINED_VOCAB_FILES_MAP
			__a    :					List[Any]     					=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__a    :					int     					=		BigBirdTokenizer
			__a    :					List[str]     					=		['''input_ids''', '''attention_mask''']
			__a    :					List[int]     					=		[]





			def __init__(  self ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  lowerCAmelCase__="<unk>" ,  lowerCAmelCase__="<s>" ,  lowerCAmelCase__="</s>" ,  lowerCAmelCase__="<pad>" ,  lowerCAmelCase__="[SEP]" ,  lowerCAmelCase__="[MASK]" ,  lowerCAmelCase__="[CLS]" ,  **lowerCAmelCase__ ,  )	->   Optional[Any]:

							'''simple docstring'''


							__lowercase					     =				AddedToken(lowerCAmelCase__ ,  lstrip=lowerCAmelCase__ ,  rstrip=lowerCAmelCase__		) if isinstance(lowerCAmelCase__ ,  lowerCAmelCase__		) else bos_token
							__lowercase					     =				AddedToken(lowerCAmelCase__ ,  lstrip=lowerCAmelCase__ ,  rstrip=lowerCAmelCase__		) if isinstance(lowerCAmelCase__ ,  lowerCAmelCase__		) else eos_token
							__lowercase					     =				AddedToken(lowerCAmelCase__ ,  lstrip=lowerCAmelCase__ ,  rstrip=lowerCAmelCase__		) if isinstance(lowerCAmelCase__ ,  lowerCAmelCase__		) else unk_token
							__lowercase					     =				AddedToken(lowerCAmelCase__ ,  lstrip=lowerCAmelCase__ ,  rstrip=lowerCAmelCase__		) if isinstance(lowerCAmelCase__ ,  lowerCAmelCase__		) else pad_token
							__lowercase					     =				AddedToken(lowerCAmelCase__ ,  lstrip=lowerCAmelCase__ ,  rstrip=lowerCAmelCase__		) if isinstance(lowerCAmelCase__ ,  lowerCAmelCase__		) else cls_token
							__lowercase					     =				AddedToken(lowerCAmelCase__ ,  lstrip=lowerCAmelCase__ ,  rstrip=lowerCAmelCase__		) if isinstance(lowerCAmelCase__ ,  lowerCAmelCase__		) else sep_token

							# Mask token behave like a normal word, i.e. include the space before it
							__lowercase					     =				AddedToken(lowerCAmelCase__ ,  lstrip=lowerCAmelCase__ ,  rstrip=lowerCAmelCase__		) if isinstance(lowerCAmelCase__ ,  lowerCAmelCase__		) else mask_token

							super().__init__(
							    lowerCAmelCase__ ,  tokenizer_file=lowerCAmelCase__ ,  bos_token=lowerCAmelCase__ ,  eos_token=lowerCAmelCase__ ,  unk_token=lowerCAmelCase__ ,  sep_token=lowerCAmelCase__ ,  pad_token=lowerCAmelCase__ ,  cls_token=lowerCAmelCase__ ,  mask_token=lowerCAmelCase__ ,  **lowerCAmelCase__ ,  )

							__lowercase					     =				vocab_file
							__lowercase					     =				False if not self.vocab_file else True





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ = None		)	->   List[int]:

							'''simple docstring'''


							__lowercase					     =				[self.sep_token_id]
							__lowercase					     =				[self.cls_token_id]
							if token_ids_a is None:
											return cls + token_ids_a + sep
							return cls + token_ids_a + sep + token_ids_a + sep





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = False		)	->   List[int]:

							'''simple docstring'''


							if already_has_special_tokens:
											if token_ids_a is not None:
															raise ValueError(
															    '''You should not supply a second sequence if the provided sequence of '''
															    '''ids is already formatted with special tokens for the model.'''		)
											return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

							if token_ids_a is None:
											return [1] + ([0] * len(lowerCAmelCase__		)) + [1]
							return [1] + ([0] * len(lowerCAmelCase__		)) + [1] + ([0] * len(lowerCAmelCase__		)) + [1]





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ = None		)	->   List[int]:

							'''simple docstring'''


							__lowercase					     =				[self.sep_token_id]
							__lowercase					     =				[self.cls_token_id]

							if token_ids_a is None:
											return len(cls + token_ids_a + sep		) * [0]
							return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ = None		)	->   Tuple[str]:

							'''simple docstring'''


							if not self.can_save_slow_tokenizer:
											raise ValueError(
											    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
											    '''tokenizer.'''		)

							if not os.path.isdir(lowerCAmelCase__		):
											logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
											return
							__lowercase					     =				os.path.join(
							    lowerCAmelCase__ ,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

							if os.path.abspath(self.vocab_file		) != os.path.abspath(lowerCAmelCase__		):
											copyfile(self.vocab_file ,  lowerCAmelCase__		)

							return (out_vocab_file,)
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
		import datasets




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			@slow
			@require_torch
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Any:

							'''simple docstring'''


							__lowercase					     =				EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny''' ,  '''prajjwal1/bert-tiny'''		)
							__lowercase					     =				BertTokenizer.from_pretrained('''bert-base-uncased'''		)

							__lowercase					     =				bertabert.config.encoder.vocab_size
							__lowercase					     =				tokenizer.sep_token_id
							__lowercase					     =				tokenizer.cls_token_id
							__lowercase					     =				1_28

							__lowercase					     =				datasets.load_dataset('''cnn_dailymail''' ,  '''3.0.0''' ,  split='''train[:1%]'''		)
							__lowercase					     =				datasets.load_dataset('''cnn_dailymail''' ,  '''3.0.0''' ,  split='''validation[:1%]'''		)

							__lowercase					     =				train_dataset.select(range(32		)		)
							__lowercase					     =				val_dataset.select(range(16		)		)

							__lowercase					     =				4

							def _map_to_encoder_decoder_inputs(lowerCAmelCase__		):
											# Tokenizer will automatically set [BOS] <text> [EOS]
											__lowercase					     =				tokenizer(batch['''article'''] ,  padding='''max_length''' ,  truncation=lowerCAmelCase__ ,  max_length=5_12		)
											__lowercase					     =				tokenizer(batch['''highlights'''] ,  padding='''max_length''' ,  truncation=lowerCAmelCase__ ,  max_length=1_28		)
											__lowercase					     =				inputs.input_ids
											__lowercase					     =				inputs.attention_mask

											__lowercase					     =				outputs.input_ids
											__lowercase					     =				outputs.input_ids.copy()
											__lowercase					     =				[
											    [-1_00 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
											]
											__lowercase					     =				outputs.attention_mask

											assert all(len(lowerCAmelCase__		) == 5_12 for x in inputs.input_ids		)
											assert all(len(lowerCAmelCase__		) == 1_28 for x in outputs.input_ids		)

											return batch

							def _compute_metrics(lowerCAmelCase__		):
											__lowercase					     =				pred.label_ids
											__lowercase					     =				pred.predictions

											# all unnecessary tokens are removed
											__lowercase					     =				tokenizer.batch_decode(lowerCAmelCase__ ,  skip_special_tokens=lowerCAmelCase__		)
											__lowercase					     =				tokenizer.batch_decode(lowerCAmelCase__ ,  skip_special_tokens=lowerCAmelCase__		)

											__lowercase					     =				sum([int(pred_str[i] == label_str[i]		) for i in range(len(lowerCAmelCase__		)		)]		) / len(lowerCAmelCase__		)

											return {"accuracy": accuracy}

							# map train dataset
							__lowercase					     =				train_dataset.map(
							    _map_to_encoder_decoder_inputs ,  batched=lowerCAmelCase__ ,  batch_size=lowerCAmelCase__ ,  remove_columns=['''article''', '''highlights'''] ,  )
							train_dataset.set_format(
							    type='''torch''' ,  columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] ,  )

							# same for validation dataset
							__lowercase					     =				val_dataset.map(
							    _map_to_encoder_decoder_inputs ,  batched=lowerCAmelCase__ ,  batch_size=lowerCAmelCase__ ,  remove_columns=['''article''', '''highlights'''] ,  )
							val_dataset.set_format(
							    type='''torch''' ,  columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] ,  )

							__lowercase					     =				self.get_auto_remove_tmp_dir()

							__lowercase					     =				SeqaSeqTrainingArguments(
							    output_dir=lowerCAmelCase__ ,  per_device_train_batch_size=lowerCAmelCase__ ,  per_device_eval_batch_size=lowerCAmelCase__ ,  predict_with_generate=lowerCAmelCase__ ,  evaluation_strategy='''steps''' ,  do_train=lowerCAmelCase__ ,  do_eval=lowerCAmelCase__ ,  warmup_steps=0 ,  eval_steps=2 ,  logging_steps=2 ,  )

							# instantiate trainer
							__lowercase					     =				SeqaSeqTrainer(
							    model=lowerCAmelCase__ ,  args=lowerCAmelCase__ ,  compute_metrics=_compute_metrics ,  train_dataset=lowerCAmelCase__ ,  eval_dataset=lowerCAmelCase__ ,  tokenizer=lowerCAmelCase__ ,  )

							# start training
							trainer.train()